Voltage collapse detection using ant colony optimization for smart grid applications

2010 ◽  
Author(s):  
C. Church ◽  
Walid G. Morsi ◽  
C. P. Diduch ◽  
M. E. El-Hawary ◽  
L. Chang
Keyword(s):  
Smart Grid ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Voltage Collapse ◽  
Grid Applications

Voltage collapse detection using Ant Colony Optimization for smart grid applications

2011 ◽  
Vol 81 (8) ◽  
pp. 1723-1730 ◽  
Author(s):  
C. Church ◽  
W.G. Morsi ◽  
M.E. El-Hawary ◽  
C.P. Diduch ◽  
L.C. Chang
Keyword(s):  
Smart Grid ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Voltage Collapse ◽  
Grid Applications

An ant colony optimization algorithm-based emergency control strategy for voltage collapse mitigation

2012 ◽  
Vol 3 (2) ◽  
pp. 147-156 ◽  
Author(s):  
R. A. El-Sehiemy ◽  
A. A. A. El Ela ◽  
A. M. M. Kinawy ◽  
M. T. Mouwafia
Keyword(s):  
Power Systems ◽  
Ant Colony Optimization ◽  
Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Ant Colony ◽  
Voltage Collapse ◽  
Ant Colony Optimization Algorithm ◽  
Preventive Control ◽  
Control Actions

Abstract This paper presents optimal preventive control actions using ant colony optimization (ACO) algorithm to mitigate the occurrence of voltage collapse in stressed power systems. The proposed objective functions are: minimizing the transmission line losses as optimal reactive power dispatch (ORPD) problem, maximizing the preventive control actions by minimizing the voltage deviation of load buses with respect to the specified bus voltages and minimizing the reactive power generation at generation buses based on control variables under voltage collapse, control and dependent variable constraints using proposed sensitivity parameters of reactive power that dependent on a modification of Fast Decoupled Power Flow (FDPF) model. The proposed preventive actions are checked for different emergency conditions while all system constraints are kept within their permissible limits. The ACO algorithm has been applied to IEEE standard 30-bus test system. The results show the capability of the proposed ACO algorithm for preparing the maximal preventive control actions to remove different emergency effects.

Parallel Ant Colony Optimization on the University Course-Faculty Timetabling Problem in MSU-IIT

2012 ◽  
Author(s):  
Earth B. Ugat ◽  
Jennifer Joyce M. Montemayor ◽  
Mark Anthony N. Manlimos ◽  
Dante D. Dinawanao
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Timetabling Problem ◽  
University Course ◽  
The University

Achievement of Cost-Effective Datasets Transference in The Cloud Through Ant Colony Optimization

2012 ◽  
Vol 3 (3) ◽  
pp. 122-125
Author(s):  
THAHASSIN C THAHASSIN C ◽  
◽  
A. GEETHA A. GEETHA ◽  
RASEEK C RASEEK C
Keyword(s):  
Ant Colony Optimization ◽  
Cost Effective ◽  
Ant Colony

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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